scholarly journals Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

2022 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Huijuan Song ◽  
Guoxing Tang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tuberculosis Infection ◽  
Routine Laboratory

Utility of QuantiFERON-TB In-Tube Test for Differentiating Active Tuberculosis from Latent Tuberculosis Infection in an Intermediate Burden Country

2008 ◽  
Vol 40 (6) ◽  
pp. 305 ◽  
Author(s):  
Suk Hoon Choi ◽  
Bum Sik Chin ◽  
Sang Hoon Han ◽  
Han Sung Lee ◽  
Chang Oh Kim ◽  
...  
Keyword(s):  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tuberculosis Infection ◽  
Tube Test

scholarly journals Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e039501
Author(s):  
Beibei Qiu ◽  
Qiao Liu ◽  
Zhongqi Li ◽  
Huan Song ◽  
Dian Xu ◽  
...  
Keyword(s):  
Latent Tuberculosis Infection ◽  
Meta Analysis ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tuberculosis Infection ◽  
Cochrane Library ◽  
Effective Vaccine ◽  
Specific Method ◽  
Case Control Studies ◽  
Ifn Γ

ObjectivesWith a marginally effective vaccine and no significant breakthroughs in new treatments, a sensitive and specific method to distinguish active tuberculosis from latent tuberculosis infection (LTBI) would allow for early diagnosis and limit the spread of the pathogen. The analysis of multiple cytokine profiles provides the possibility to differentiate the two diseases.DesignSystematic review and meta-analysis.Data sourcesPubMed, Cochrane Library, Clinical Key and EMBASE databases were searched on 31 December 2019.Eligibility criteriaWe included case–control studies, cohort studies and randomised controlled trials considering IFN-γ, TNF-α, IP-10, IL-2, IL-10, IL-12 and VEGF as biomarkers to distinguish active tuberculosis and LTBI.Data extraction and synthesisTwo students independently extracted data and assessed the risk of bias. Diagnostic OR, sensitivity, specificity, positive and negative likelihood ratios and area under the curve (AUC) together with 95% CI were used to estimate the diagnostic value.ResultsOf 1315 records identified, 14 studies were considered eligible. IL-2 had the highest sensitivity (0.84, 95% CI: 0.72 to 0.92), while VEGF had the highest specificity (0.87, 95% CI: 0.73 to 0.94). The highest AUC was observed for VEGF (0.85, 95% CI: 0.81 to 0.88), followed by IFN-γ (0.84, 95% CI: 0.80 to 0.87) and IL-2 (0.84, 95% CI: 0.81 to 0.87).ConclusionCytokines, such as IL-2, IFN-γ and VEGF, can be utilised as promising biomarkers to distinguish active tuberculosis from LTBI.PROSPERO registration numberCRD42020170725.

Biomarkers for discrimination between latent tuberculosis infection and active tuberculosis disease

2017 ◽  
Vol 74 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Eun-Jeong Won ◽  
Jung-Ho Choi ◽  
Young-Nan Cho ◽  
Hye-Mi Jin ◽  
Hae Jin Kee ◽  
...  
Keyword(s):  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tuberculosis Infection ◽  
Tuberculosis Disease
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